Two-phase flow pattern transition behaviors on experimental established ordinal pattern networks.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-05-01 DOI:10.1063/5.0254994
Meng Du, Zhenqian Zhang, Yang Cao, Yuliang Liu, Weidong Cao, Zhong-Ke Gao
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引用次数: 0

Abstract

Identifying the flow pattern transition dynamics is a fundamental challenge in modeling a two-phase flow system. In this paper, we investigate the gas-liquid two-phase flow pattern transition behaviors with analyzing the topology structures of the experimental established gas-liquid two-phase flow complex networks. First, we carry out a series of gas-liquid two-phase flow experiments in a vertical 50 mm inner diameter pipe. During the experiments, the two-phase flow fluctuation signals are collected and used to establish the ordinal pattern complex networks, which represent different flow patterns. Then, we employ a K-core decomposition method to identify the hierarchical structures of our established flow pattern networks. We find that the decay rate of the K-core size is sensitive to the flow conditions and can be a potential metric for identifying the flow pattern transitions. Additionally, we analyze the network homology persistence, which indicates the loop structures in the flow pattern networks. The persistence indexes-maximum persistence and persistence entropy-are used to investigate the flow pattern oscillatory behaviors along with the flow pattern transitions. This research provides a novel way for investigating the flow pattern transition behaviors of a gas-liquid two-phase flow system, which are expected to be applicable in other complex fluid systems.

两相流型转换行为在实验建立的有序型网上。
确定流型转换动力学是两相流系统建模的一个基本挑战。本文通过分析实验建立的气液两相流复杂网络的拓扑结构,研究气液两相流型转变行为。首先,我们在一个内径为50mm的垂直管道中进行了一系列气液两相流实验。在实验过程中,采集两相流波动信号,建立代表不同流型的有序模式复杂网络。然后,我们采用k核分解方法来识别我们所建立的流型网络的层次结构。我们发现,k核尺寸的衰减率对流动条件很敏感,可以作为识别流型转变的潜在指标。此外,我们还分析了网络同源持久性,这表明了流型网络中的环路结构。利用持续指数——最大持续值和持续熵——研究了随着流型转变的流型振荡行为。该研究为研究气液两相流系统的流型转变行为提供了一种新的途径,有望应用于其他复杂流体系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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